As live shopping increasingly becomes a mainstream trend, the demand for skilled live hosts far exceeds the supply, with a significant disparity in sales effectiveness between top-tier and average hosts. In response to this challenge, the project proposes a solution that combines high-quality pre-recorded content with real-time interactive capabilities. This approach addresses the lack of audience interaction in traditional pre-recorded content, which is crucial for enhancing user engagement. Our intelligent system seamlessly integrates video segments, intelligently determining the timing for each segment based on live audience interactions. This blend of pre-recorded content’s richness with the flexibility of live responses not only enhances the shopping experience but also maximizes sales opportunities through personalized content delivery. Additionally, the project employs large language model (LLM) technology, combined with multi-agent systems and automated prompt optimization, to intelligently enhance marketing outcomes and reduce manual effort. This system aims to build an efficient and flexible solution for live shopping scenarios. The project is currently in its initial phase, with the feasibility study completed, and development is set to begin soon.
How it is being developed
1.Video Processing & Real-Time Integration Tools： Video Stream Management: FFmpeg and GStreamer for format conversion, segmentation, and real-time streaming of pre-recorded content.
Interaction Data Integration: WebRTC or Socket.IO for low-latency transmission of audience interactions (e.g., comments, likes, polls).
Cloud Infrastructure: AWS/AliCloud for scalable computing and storage to handle high-concurrency scenarios
2.Intelligent Content Scheduling Technology：
Dynamic Segment Orchestration Apache Kafka/RabbitMQ for real-time analysis of audience behavior (e.g., dwell time, interaction frequency), combined with reinforcement learning (TensorFlow/PyTorch) to optimize playback logic.
LLM-Driven Interaction Enhancement: Hugging Face Transformers/OpenAI API integrated with multi-agent systems (Autogen/MetaGPT) to generate real-time sales scripts, enhanced by automated prompt optimization (AutoPrompt) for higher conversion rates.
Expected outcome
This project will empower live commerce by integrating pre-recorded content with real-time interactions to create an intelligent 24/7 streaming solution. Key targets include boosting user engagement by 40%, achieving 80% of top-tier hosts’ sales conversion rates, and automating 90% of interaction scripts via LLM and multi-agent systems to reduce manual efforts by 70%. The system supports 100,000+ concurrent interactions per session and non-stop streaming, cutting operational costs by 60% compared to traditional models, thereby driving the industry’s shift from labor dependence to technology-driven operations.
Brief Introduction
As live shopping increasingly becomes a mainstream trend, the demand for skilled live hosts far exceeds the supply, with a significant disparity in sales effectiveness between top-tier and average hosts. In response to this challenge, the project proposes a solution that combines high-quality pre-recorded content with real-time interactive capabilities. This approach addresses the lack of audience interaction in traditional pre-recorded content, which is crucial for enhancing user engagement. Our intelligent system seamlessly integrates video segments, intelligently determining the timing for each segment based on live audience interactions. This blend of pre-recorded content’s richness with the flexibility of live responses not only enhances the shopping experience but also maximizes sales opportunities through personalized content delivery. Additionally, the project employs large language model (LLM) technology, combined with multi-agent systems and automated prompt optimization, to intelligently enhance marketing outcomes and reduce manual effort. This system aims to build an efficient and flexible solution for live shopping scenarios. The project is currently in its initial phase, with the feasibility study completed, and development is set to begin soon.
How it is being developed
1.Video Processing & Real-Time Integration Tools： Video Stream Management: FFmpeg and GStreamer for format conversion, segmentation, and real-time streaming of pre-recorded content.
Interaction Data Integration: WebRTC or Socket.IO for low-latency transmission of audience interactions (e.g., comments, likes, polls).
Cloud Infrastructure: AWS/AliCloud for scalable computing and storage to handle high-concurrency scenarios
2.Intelligent Content Scheduling Technology：
Dynamic Segment Orchestration Apache Kafka/RabbitMQ for real-time analysis of audience behavior (e.g., dwell time, interaction frequency), combined with reinforcement learning (TensorFlow/PyTorch) to optimize playback logic.
LLM-Driven Interaction Enhancement: Hugging Face Transformers/OpenAI API integrated with multi-agent systems (Autogen/MetaGPT) to generate real-time sales scripts, enhanced by automated prompt optimization (AutoPrompt) for higher conversion rates.
Expected outcome
This project will empower live commerce by integrating pre-recorded content with real-time interactions to create an intelligent 24/7 streaming solution. Key targets include boosting user engagement by 40%, achieving 80% of top-tier hosts’ sales conversion rates, and automating 90% of interaction scripts via LLM and multi-agent systems to reduce manual efforts by 70%. The system supports 100,000+ concurrent interactions per session and non-stop streaming, cutting operational costs by 60% compared to traditional models, thereby driving the industry’s shift from labor dependence to technology-driven operations.